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recall_relevant

Recall the most semantically relevant stored fragments using BM25 ranking with a feedback loop that boosts previously successful outputs. Returns a ranked pointer list with source, score, and snippet.

Instructions

Semantic recall of the most relevant stored fragments.

Uses BM25 relevance ranking (recall_auto) with a feedback loop (fragments that previously led to successful outputs are boosted).

Returns a slim ranked pointer list by default — source, score, and a locating snippet — because full fragment bodies overflow the tool result cap (a top_k=8 recall is ~90KB). Pass full=True only when you need the complete text of every hit.

Args: query: The search query top_k: Number of results to return full: Return complete fragment bodies instead of the slim view

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fullNo
queryYes
top_kNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses key behavioral traits: default slim pointer list, BM25 plus feedback loop, result size cap, and reasoning for default view. No annotations exist, so description carries full burden, which it handles well.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with summary first, then details. Each sentence serves a purpose—explaining algorithm, default behavior, parameter guidance. Could be slightly tighter but effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given an output schema exists, the description appropriately omits return value details but covers default slim output and full option. Addresses result size limitation. Missing edge cases like empty query but overall complete for the tool's purpose.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds meaning beyond schema: explains the 'full' parameter effect, default value, and the rationale (overflow cap). Query and top_k are mentioned but less detailed; still adds value over bare schema names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it performs semantic recall of stored fragments, mentioning BM25 ranking and feedback loops. It distinguishes its default slim output from the full option, but does not explicitly differentiate from similar sibling tools like vault_search or entroly_retrieve.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear guidance on when to use the full parameter, explaining the result size limitation and recommending against it unless full text is needed. However, no explicit when-not-to-use or alternatives are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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